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A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

The brain–computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current topic; however, to the best of our knowledge, this is the first attempt to highlight these challenges and open issues in signal processing methods, techniques and pattern recognition in smart BCI training environments. This work illustrates the effect of the theoretical perspectives associated with BCI works for research development in smart training environments. Consequently, this research copes with these issues via a systematic review protocol to help the large community of BCI users, especially people with disabilities. Fundamentally, four substantial databases, namely, IEEE, ScienceDirect, Scopus and PubMed contain a considerable amount of technical and scientific articles relevant to smart BCI training systems. A set of 375 articles is collected from 2010 to 2020 to reveal a clear picture and a better understanding of all the academic literature through a final set of 25 articles. In addition, this research provides the state of the art for signal processing, feature extraction, classification techniques and smart training environment characteristics for MI-based BCI applications. This study also reports the challenges and issues identified by the researchers as well as recommended solutions to solve the persistent problems. This study introduces the state-of-the art virtual and augmented reality environments as a smart platform and the neurofeedback schemes used for MI-based smart BCI training systems. Moreover, this study highlights for the first time 10 concepts of smart training in a virtual environment applied in MI and BCI, and investigates the evaluation of these concepts against the literature to gain only 45.55%. Collectively, the implication of this study will offer the opportunity of deploying an efficient smart BCI training system in terms of data acquisition and recording, pattern recognition and smart environment for BCI users and rehabilitation programmes.

Medical institutions face serious problems, such as growing elderly population and lack of doctors. Telemedicine and remote health monitoring system (RHMS) intend to tackle these problems by slightly shortening hospital stays. RHMS reduces the burden on patients with primary care and improves communication among different health units to reduce the burden on emergency departments. Several healthcare studies have attempted to replace hospital visits with RHMS to deliver triage and prioritisation for patients because of considerable advances in wireless information communication and signal-processing technology. The process of medical triage determines the severity of a patient’s situation, whilst prioritisation is carried out to provide healthcare services for patients in due course to save their lives. An essential investigation is required to highlight the drawbacks of the current situation of patient triage and prioritisation over telemedicine environment. In this paper, a systematic review of medical emergency triage and patient prioritisation in a telemedicine environment was presented on the basis of two critical directions. Firstly, previous studies on patient triage and prioritisation in such an environment were collected, analysed and categorised. Secondly, many standards and guidelines of triage and different methods and techniques of prioritisation were presented and reviewed in detail. The following results were obtained: (1) The limitations and problems of existing patient triage and prioritisation methods were presented and emphasised. (2) The combination of triage and prioritisation of patients with chronic heart disease was not presented. (3) A framework based on evidence theory and integration of multilayer analytical hierarchy process and technique for order of preference by similarity to ideal solution methods can be used in the future in order to triage chronic heart disease patients into different emergency levels and prioritise many patients to receive emergency and treatment-based services.

Recent years have shown significantly pervasive interest in mobile applications (hereinafter “apps”). The number and popularity of these apps are dramatically increasing. Even though mobile apps are diverse, countless ones are available through many platforms. Some of these apps are not useful nor do they possess rich content, which benefits end users as expected, especially in medical-related cases. This research aims to review and analyze articles associated with medical app assessment across different platforms. This research also aimed to provide the best practices and identify the academic challenges, motivations and recommendations related with quality assessments. In addition, a methodological approach followed in previous research in this domain was also discussed to give some insights for future comers with what to expect. We systematically searched articles on topics related to medical app assessment. The search was conducted on five major databases, namely, Science Direct, Springer, Web of Science, IEEE Xplore and PubMed from 2009 to September 2019. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected on the basis of our inclusion and exclusion criteria (n = 72). Medical app assessment is considered a major topic which warrants attention. This study emphasizes the current standpoint and opportunities for research in this area and boosts additional efforts towards the understanding of this research field.

Electronic medical record (EMR) is currently a popular topic in e-health. EMR includes the health-related information of patients and forms the main factor of e-health applications. Moreover, EMR contains the legal records that are created in the medical centre and ambulatory environments. These records serve as the data source for electronic health record. Although hospitals utilise the EMR system, healthcare professionals experience difficultly in trusting this system. Studies devoted to EMR acceptance in hospitals are lacking, particularly those on the EMR system in the contexts of privacy and security concerns based on multi-criteria perspective. Thus, the current study proposes a decision support examination framework on how individual, security and privacy determinants influence the acceptance and use of EMR. The proposed framework is based on a multi-criteria perspective derived from healthcare professionals in Malaysia as frame of reference. The framework comprises four phases. The sub-factors of individual, security and privacy determinants were investigated in the two initial phases. Thereafter, the sub-factors were identified with uniform multi-criteria perspective to establish a decision matrix. The decision matrix used individual uniform as basis to cluster the sub-factors and user perspectives. Subsequently, a new ‘multi-criteria decision-making (MCDM) approach’ was adopted. Integrated technique for order of preference by similarity (TOPSIS) and analytic hierarchy process (AHP) were used as bases in employing the MCDM approach to rank each group of factors. K-means clustering was also applied to identify the critical factors in each group. Healthcare professionals in Malaysia were selected as respondents and 100 questionnaires were distributed to those employed in 5 Malaysian public hospitals. A conceptual model adapted from Unified theory of acceptance and use of technology 2 (UTAUT2) was employed to clarify the connection between individual, privacy and security determinants and EMR system acceptance and use in the selected context. After collecting the data sets (363), structural equation modelling was used to analyse data related to EMR acceptance and use. Results are as follows. (1) Five determinants (i.e. data integrity, confidentiality, non-repudiation, facilitating conditions and effort expectancy) exerted an explicit and important positive effect on EMR acceptance and use. (2) Three determinants (i.e. unauthorised, error and secondary use) exerted a direct and significant negative effect on EMR acceptance and use. (3) Three other determinants (i.e. authentication, performance expectancy and habit) insignificantly affected the behavioural intention of healthcare experts in Malaysia to use EMR.

This research aims to review the attempts of researchers in response to the new and disruptive technology of skin cancer applications in terms of evaluation and benchmarking, in order to identify the research landscape from the literature into a cohesive taxonomy. An extensive search was conducted for articles dealing with ‘skin cancer’, ‘apps’ and ‘smartphone’ or ‘mHealth’ in different variations to find all the relevant articles in three main databases, namely, “Web of Science”, “Science Direct”, and “IEEE explore”. These databases are considered wide enough to cover medical and technical literature. The final classification scheme outcome of the dataset contained 110 articles that were classified into four classes: development and design; analytical; evaluative and comparative; and review and survey studies. Afterwards, another filtering process was achieved based on the evaluation criteria error rate within the dataset, time complicity and reliability, which are used in skin cancer applications. The final classification scheme outcome of the dataset contained 89 articles distributed in mapping and crossover with four sections concluded from 110 articles. Development and design studies, analytical studies, evaluative and comparative studies and articles of reviews and surveys comprised of 48.3146%, 22.4719%, 16.8539% (15), and 12.3595% (11) of the reviewed articles, respectively. The basic features of this evolving approach were identified in these aspects. We also determined open issues in terms of evaluation and benchmarking that hamper the utility of this technology. Furthermore, with the exception of the 89 papers reviewed, the new recommendation pathway solution was described in order to improve the measurement process for smartphone-based skin cancer diagnosis applications.